Dynamics of a mathematical model for cancer therapy with oncolytic viruses

Actually, cancer is considered one of the leading causes of death in the world. Various therapeutic strategies have been developed to combat this dangerous disease. This article investigates a promising therapeutic strategy by proposing a mathematical model that describes the dynamics of cancer treatment with oncolytic viruses. The proposed model integrates the time needed for infected tumor cells to produce new virions after viral entry, the probability of surviving during the latent period, and the saturation effect. We first prove the well-posedness of model and the existence of three equilibria that represent the desired outcome of therapy, the complete failure of therapy and the partial success of therapy. Furthermore, the stability analysis of equilibria and the existence of Hopf bifurcation are rigourously investigated.

• Publication year

  2019

• Venue

  Communications in Mathematical Biology and Neuroscience

• Publication date

  2019-05-16

• Fields of study

  Mathematics, Medicine

• Identifiers
  DOI 10.28919/CMBN/3995
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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• The replicability of oncolytic virus: defining conditions in tumor virotherapy.

  2011influential reference
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